Staff Data Scientist, Firefox

Responsibilities

• Own the analytical strategy for a product area: identifying the highest-leverage questions, defining the measurement framework, and ensuring data-driven decisions
• Serve as a strategic partner to product and engineering leadership, translating ambiguous business problems into analytical approaches and clear, actionable recommendations
• Define north-star metrics and measurement strategies to set goals, evaluate progress, and make trade-offs
• Design and oversee experiments and causal analyses, ensuring methodological rigor and that results drive real product decisions
• Develop and maintain a deep understanding of user growth dynamics: how acquisition, activation, and retention interact to drive growth, and use that understanding to diagnose metric movements, explain trends to leadership, and anticipate emerging risks or opportunities
• Contribute and own areas of the team’s forecasting and growth modeling efforts, helping translate statistical models into actionable growth strategies
• Mentor and elevate other data scientists through code review, methodology guidance, and establishing reusable analytical frameworks
• Represent data science in cross-functional forums, making the case for what the data shows even when it challenges prevailing assumptions
• Drive alignment across data science, data engineering, and product on shared priorities like data quality, metric definitions, and instrumentation

Requirements

• 8+ years of experience in data science, analytics, or applied quantitative analysis, with a track record of shaping product strategy through data
• Demonstrated ability to lead complex, cross-functional analytical initiatives from problem framing through stakeholder alignment to decision
• Deep expertise in experimentation (A/B testing) and causal inference, with strong judgment about when each method applies and what conclusions they support
• Advanced proficiency in SQL and Python for analysis, modeling, and validation
• Experience defining and owning product metrics that teams actually use to make decisions
• Strong opinions, loosely held: you can take a position on what the data says, advocate for it clearly, and update when the evidence changes
• Track record of mentoring or technically leading other data scientists